by Dr. Reed Ferber, Ph.D. CAT(C)
“Running safety is an example of an important topic for women in particular,” says Marion Hart, the For Women Only training program instructor at Winnipeg’s Regent Avenue store. “We talk about ways to ‘run smart’, such as letting someone know when and where you’re running, carrying a phone and identification with you, and not running alone at night.”
Marion has seen first-hand the benefits of the women’s only program and the camaraderie that grows from it. “It is a safe environment for women who are new to running as a form of exercise, including mature-aged women, larger-bodied women, and women returning to running after cancer treatments,” she says. “The group members nurture and mother each other. This builds strong bonds and encourages participants to stick with the program.”
In the 17 For Women Only programs she has instructed, Marion says she has been part of inspiring success stories and wonderful friendships. “Many of the women have repeated the program multiple times because they enjoy the clinic’s feeling of community,” she explains.
Wearable technology is a growing multi-billion dollar industry thanks to the availability of cost-effective, highly accurate wearable devices. It’s rare to see a runner who is not wearing a Fitbit, Garmin, or Apple Watch—to name only a few—to track mileage and record metrics. In this issue, I thought I would talk about our research with wearable technology products and the unique ways we’re using them to prevent running injuries.
The good news is that wearable devices generate a profound amount of scientific data. The bad news is that most of the data is largely ignored. If the information provided is not placed in proper context, it is not going to help a runner avoid injury or make evidence-based decisions on how to train effectively. To that end, for the past five years my research lab at the University of Calgary has been focused on a concept that we call “Movement Mapping.”
We are all aware of how a car’s “check engine” light illuminates when something unusual about the car’s performance is detected. The driver of the
car is usually not exactly sure why the light has come on, but he or she knows the car needs to be inspected. In similar terms, Movement Mapping aims to alert a runner that he or she may be on the verge of an injury, or exhibiting an “atypical” biomechanical pattern. In turn, the runner is prompted to seek further advice and attention. This paradigm consists of two critical questions. First, how do we measure a runner’s typical running pattern? Second, how do we define ‘atypical’ running patterns that would necessitate a warning?
Most wearable devices measure some type of biomechanical variable, such as your cadence, step length, or how much you move up and down (vertical oscillation). The more data we collect, the more accurate and detailed we can be about the boundaries of typical motion. However, because there are so many external factors to consider, we’ve conducted extensive research to understand how your ‘typical’ pattern is influenced by factors such as changes in weather patterns, running uphill and downhill, and running faster or slower. Each of these factors result in very specific, yet individual changes in your overall ‘typical’ pattern. However, by combining these pieces of information together, we can define the borders of your ‘typical’ movement pattern.
We also know that each runner has a specific running pattern and if we collect data from many runners, we can construct a ‘Movement Map.’ For example, we collected wearable data from 41 runners last summer. About half of them were experienced runners while the other half were inexperienced. In total, data for 724 runs were collected and thousands of footfalls from each runner were used to create the Movement Map on the facing page.
Each individual dot is an individual’s pattern for one run and I’ve chosen an experienced female runner for this example. Using some sophisticated math, we can draw a boundary around the various runs for this runner (shown as “T”) in order to define her typical pattern. We can also define a boundary to separate and better define the experienced and inexperienced groups of runners.
Here’s where it gets interesting. The “Y” represents a Yellow Light situation where one Sunday this runner began to run ‘atypically’ and outside of her usual pattern, but she was still similar to the other experienced runners within the Map. The “R” represents a Red Light situation where the following Sunday, she not only ran in an atypical manner but her pattern resembled the female inexperienced runners. Interestingly, on this particular run she began
to experience knee pain and had to take a few weeks off from training. Could we have alerted this runner a week prior when the Yellow Light was flashing? Could we have helped her avoid this injury? We think the answer is yes, but we still have some research to conduct. Regardless, we think we’re on the right track in using wearable technology to help runners avoid injury and make good decisions about their training.